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de Borja, R.

Publications and source records attributed to de Borja, R..

4 recordsLinked to original sources

Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling

Building predictive models of the cell requires systematically mapping how perturbations reshape each cells state, function, and behavior. Here, we present Tahoe-100M, a giga-scale single-cell atlas of 100 million transcriptomic profiles measuring how each of 1,100 small-molecule perturbations impact cells across 50 cancer cell lines. Our high-throughput Mosaic platform, composed of a highly diverse and optimally balanced "cell village", reduces batch effects and enables parallel profiling of thousands of conditions at single-cell resolution at an unprecedented scale. As the largest single-cell dataset to date, Tahoe-100M enables artificial-intelligence (AI)-driven models to learn context-dependent functions, capturing fundamental principles of gene regulation and network dynamics. Although we leverage cancer models and pharmacological compounds to create this resource, Tahoe-100M is fundamentally designed as a broadly applicable perturbation atlas and supports deeper insights into cell biology across multiple tissues and contexts. By publicly releasing this atlas, we aim to accelerate the creation and development of robust AI frameworks for systems biology, ultimately improving our ability to predict and manipulate cellular behaviors across a wide range of applications.

genomics↗

CanDIG: Secure Federated Genomic Queries and Analyses Across Jurisdictions

Rapid expansions of bioinformatics and computational biology have broadened the collection and use of -omics data including genomic, transcriptomic, methylomic and a myriad of other health data types, in the clinic and the laboratory. Both clinical and research uses of such data require co-analysis with large datasets, for which participant privacy and the need for data custodian controls must remain paramount. This is particularly challenging in multi-jurisdictional settings, such as Canada, where health privacy and security requirements are often heterogeneous. Data federation presents a solution to this, allowing for integration and analysis of large datasets from various sites while abiding by local policies. The Canadian Distributed Infrastructure for Genomics platform (CanDIG) enables federated querying and analysis of -omics and health data while keeping that data local and under local control. It builds upon existing infrastructures to connect five health and research institutions across Canada, relies heavily on standards and tooling brought together by the Global Alliance for Genomics and Health (GA4GH), implements a clear division of responsibilities among its participants and adheres to international data sharing standards. Participating researchers and clinicians can therefore contribute to and quickly access a critical mass of -omics data across a national network in a manner that takes into account the multi-jurisdictional nature of our privacy and security policies. Through this, CanDIG gives medical and research communities the tools needed to use and analyze the ever-growing amount of -omics data available to them in order to improve our understanding and treatment of various conditions and diseases. CanDIG is being used to make genomic and phenotypic data available for querying across Canada as part of data sharing for five leading pan-Canadian projects including the Terry Fox Comprehensive Cancer Care Centre Consortium Network (TF4CN) and Terry Fox PRecision Oncology For Young peopLE (PROFYLE), and making data from provincial projects such as POG (Personalized Onco- Genomics) more widely available.

genomics↗

Identifying TCDD-resistance genes via murine and rat comparative genomics and transcriptomics

The aryl hydrocarbon receptor (AHR) mediates many of the toxic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). However, the AHR alone is insufficient to explain the widely different outcomes among organisms. Attempts to identify unknown factor(s) have been confounded by genetic variability of model organisms. Here, we evaluated three transgenic mouse lines, each expressing a different rat AHR isoform (rWT, DEL, and INS), as well as C57BL/6 and DBA/2 mice. We supplement these with whole-genome sequencing and transcriptomic analyses of the corresponding rat models: Long-Evans (L-E) and Han/Wistar (H/W) rats. These integrated multi-species genomic and transcriptomic data were used to identify genes associated with TCDD-response phenotypes.\n\nWe identified several genes that show consistent transcriptional changes in both transgenic mice and rats. Hepatic Pxdc1 was significantly repressed by TCDD in C57BL/6, rWT mice, and in L-E rat. Three genes demonstrated different AHRE-1 (full) motif occurrences within their promoter regions: Cxxc5 had fewer occurrences in H/W, as compared with L-E; Sugp1 and Hgfac (in either L-E or H/W respectively). These genes also showed different patterns of mRNA abundance across strains.\n\nThe AHR isoform explains much of the transcriptional variability: up to 50% of genes with altered mRNA abundance following TCDD exposure are associated with a single AHR isoform (30% and 10% unique to DEL and rWT respectively following 500 g/kg TCDD). Genomic and transcriptomic evidence allowed identification of genes potentially involved in phenotypic outcomes: Pxdc1 had differential mRNA abundance by phenotype; Cxxc5 had altered AHR binding sites and differential mRNA abundance.\n\nAuthor SummaryEnvironmental contaminants such as dioxins cause many toxic responses, anything from chloracne (common in humans) to death. These toxic responses are mostly regulated by the Ahr, a ligand-activated transcription factor with roles in drug metabolism and immune responses, however other contributing factors remain unclear. Studies are complicated by the underlying genetic heterogeneity of model organisms. Our team evaluated a number of mouse and rat models, including two strains of mouse, two strains of rat and three transgenic mouse lines which differ only at the Ahr locus, that present widely different sensitivities to the most potent dioxin: 2,3,7,8 tetrachlorodibenzo-p-dioxin (TCDD). We identified a number of changes to gene expression that were associated with different toxic responses. We then contrasted these findings with results from whole-genome sequencing of the H/W and L-E rats and found some key genes, such as Cxxc5 and Mafb, which might contribute to TCDD toxicity. These transcriptomic and genomic datasets will provide a valuable resource for future studies into the mechanisms of dioxin toxicities.

genomics↗

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

bioinformatics↗